def upconv2x2(in_channels, out_channels, mode='transpose'):
 if mode == 'transpose':
  # 这个上采用需要设置其输入通道，输出通道.其中kernel_size、stride
  # 大小要跟对应下采样设置的值一样大小。这样才可恢复到相同的wh。这里时反卷积操作。
  return nn.ConvTranspose2d(
   in_channels,
   out_channels,
   kernel_size=2,
   stride=2)
 else:
  # out_channels is always going to be the same
  # as in_channels
  # 这里不会改变通道数，其中scale_factor是上采用的放大因子，其是相对于当前的
  # 输入大小的倍数
  return nn.Sequential(
   nn.Upsample(mode='bilinear', scale_factor=2, align_corners=True))
  # 这里的代码是在这里设置多一个卷积,这样子就起到了可以修改其输出通道的功能了。
  # 相当于功能跟ConvTranspose2d()差不多，只是上采样的方法不同
  conv1x1((in_channels, out_channels))
 
 
def conv1x1(in_channels, out_channels, groups=1):
 return nn.Sequential(nn.Conv2d(
  in_channels,
  out_channels,
  kernel_size=1,
  groups=groups,
  stride=1),
 nn.BatchNorm2d(out_channels))

  # Stage 5
  x51 = F.relu(self.bn51(self.conv51(x4p)))
  x52 = F.relu(self.bn52(self.conv52(x51)))
  x53 = F.relu(self.bn53(self.conv53(x52)))
  #这个id5记录的是池化操作时最大值的index，其要设置参数return_indices为True
  x5p, id5 = F.max_pool2d(x53,kernel_size=2, stride=2,return_indices=True)
 
 
  # Stage 5d
  #这个是进行最大值上采样的函数，其是根据id5来把值放到什么位置，其它位置没有值的地方
  补0
  x5d = F.max_unpool2d(x5p, id5, kernel_size=2, stride=2)
  x53d = F.relu(self.bn53d(self.conv53d(x5d)))
  x52d = F.relu(self.bn52d(self.conv52d(x53d)))
  x51d = F.relu(self.bn51d(self.conv51d(x52d)))

#测试上采样
m=nn.MaxPool2d((3,3),stride=(1,1),return_indices=True)
upm=nn.MaxUnpool2d((3,3),stride=(1,1))
data4=torch.randn(1,1,3,3)
output5,indices=m(data4)
output6=upm(output5,indices)
 
print('\ndata4:',data4,
  '\nmaxPool2d',output5,
  '\nindices:',indices,
  '\noutput6:',output6)

data4: tensor([[[[ 2.3151, -1.0391, 0.1074],
   [ 1.9360, 0.2524, 2.3735],
   [-0.1151, 0.4684, -1.8800]]]]) 
maxPool2d tensor([[[[2.3735]]]]) 
indices: tensor([[[[5]]]]) 
output6: tensor([[[[0.0000, 0.0000, 0.0000],
   [0.0000, 0.0000, 2.3735],
   [0.0000, 0.0000, 0.0000]]]])